Intelligent phishing detection system for e-banking using fuzzy data mining

Abstract

Abstract-Detecting and identifying e-banking Phishing websites is really a complex and dynamic problem involving many factors and criteria. Because of the subjective considerations and the ambiguities involved in the detection, Fuzzy Data Mining Techniques can be an effective tool in assessing and identifying e-banking phishing websites since it offers a more natural way of dealing with quality factors rather than exact values. In this paper, we present novel approach to overcome the "fuzziness" in the e-banking phishing website assessment and propose an intelligent resilient and effective model for detecting e-banking phishing websites. The proposed model is based on Fuzzy logic combined with Data Mining algorithms to characterize the e-banking phishing website factors and to investigate its techniques by classifying there phishing types and defining six e-banking phishing website attack criteria"s with a layer structure. A Case study was applied to illustrate and simulate the phishing process. Our experimental results showed the significance and importance of the e-banking phishing website criteria (URL & Domain Identity) represented by layer one, and the variety influence of the phishing characteristic layers on the final e-banking phishing website rate.

An Intellect Learning on E-Mail Security and Fraud, Spam and Phishing

“…Hossain et al [22] aimed the fuzzy data mining e-banking phishing website exemplary which exhibitedconnotation and prominence of the e-banking phishing website criteria (URL & Domain Identity). It also revealed that even if some of the e-banking phishing website physiognomies or stratums are not very vibrant or not convinced, the website can still be phishy particularly when other phishing physiognomies or stratums are evident and flawless.…”

Intelligent rule‐based phishing websites classification

“…There was insignificant impact of the "Page Style" on "Social Human Factor" related features on the accuracy. Later in 2010 (13), the authors used the 27 features to build a model based on fuzzy-logic. Although, this is a promising solution it lacks to clarify how the features were extracted from the website, specifically features related to human-factors.…”

Predicting phishing websites based on self-structuring neural network

“…There was insignificant impact of the "Page Style" on "Social Human Factor criteria". Later on [18], the authors used the 27 features to build a model to predict websites type based on fuzzy data mining. Although, their method is a promising solution it did not clarify how the features were extracted from the website and specifically features related to human factors "Much Emphasis on Security and Response, Generic Salutation and Buying Time to Access Accounts".…”